Marcus Buckmann, Galina Potjagailo and Philip Schnattinger

Disentangling the sources of high inflation, exceeding inflation targets in the post- pandemic period, has been a priority for monetary policy makers. We use machine learning for this task – a boosted decision tree model that fits non-linear associations between many indicators and inflation. We add economic interpretability by categorising the data into intuitive blocks representing components of the Phillips curve. To further disentangle inflation drivers, we separate the signals that reflect demand and supply by imposing sign-restrictions on the decision trees. Our model tells us that both global supply and domestic demand spurred UK CPI inflation post-pandemic. We detect important non-linearities: in the Phillips curve relationship with labour market tightness and unemployment and via additional effects from short-term inflation expectations.
Continue reading “Boosted inflation – using machine learning to make sense of non-linear determinants of inflation”







